Early risk prediction of diabetes could help doctors and patients to pay attention to the disease and intervene as soon as possible, which can effectively reduce the risk of complications. In this paper, a GA-stacking ensemble learning model is proposed to improve the accuracy of diabetes risk prediction. Firstly, genetic algorithms (GA) based on Decision Tree (DT) is used to select individuals with high adaptability, that is, a subset of attributes suitable for diabetes risk prediction. Secondly, the optimized convolutional neural network (CNN) and support vector machine (SVM) are used as the primary learners of stacking to learn attribute subsets, respectively. Then, the output of CNN and SVM is used as the input of the mate learner, the fully connected layer, for classification. Qingdao desensitization physical examination data from 1 January 2017 to 31 December 2019 is used, which includes body temperature, BMI, waist circumference, and other indicators that may be related to early diabetes. We compared the performance of GA-stacking with K-nearest neighbor (KNN), SVM, logistic regression (LR), Naive Bayes (NB), and CNN before and after adding GA through the average prediction time, accuracy, precision, sensitivity, specificity, and F1-score. Results show that prediction efficiency can be improved by adding GA. GA-stacking has higher prediction accuracy. Moreover, the strong generalization ability and high prediction efficiency of GA-stacking have also been verified on the early-stage diabetes risk prediction dataset published by UCI.

Hypertension is a common chronic disease, and its high incidence and complications bring heavy burden to patients and society. In order to effectively control hypertension and reduce the risk of cardiovascular disease, this paper puts forward a set of hypertension prevention and control scheme for a community in Laoshan District of Qingdao City, and discusses the supporting role of information construction. The program takes health education as the basis, lifestyle intervention as the means, standardized diagnosis and treatment as the core, drug treatment as the guarantee, health monitoring as the support, and social support as the link, forming an all-round, multi-level and three-dimensional hypertension prevention and control system. In the process of the project, our medical team and information technology team cooperated closely to develop software and algorithms, including health management system, underlying database, app and intelligent background system, which made our project have tools, challenges and output.